Abstract: Industrial steel surface defects exhibit pronounced anisotropic texture characteristics with large intra-class variation, yet existing real-time detection methods lack effective perception mechanisms for such directional local patterns in feature pyramid networks. This paper proposes a direction-aware heterogeneous convolution feature enhancement method based on RT-DETR, incorporating three key technical contributions. First, a Direction-Aware Sparse Convolution (DASC) kernel is designed, which partitions input channels into five directional groups with fixed sparse spatial masks to achieve parallel perception of right, left, down, up, and center directional textures at approximately 11.5% of the computational cost of equivalent standard convolutions. Second, a Direction-aware Interaction and Refinement (DIR) bottleneck is constructed using an expand-activate-compress dual-layer DASC structure to realize hierarchical fusion of directional features across channels, forming the complete Lightweight Feature Enhancement module with Cross-stage 3 modules for RT-DETR (LFEC3-RT). Third, a Cross-scale FPN Consistent Deployment (CFPD) strategy is introduced, globally deploying LFEC3-RT across all four fusion positions in the feature pyramid to eliminate cross-scale feature style inconsistency caused by selective deployment. Experiments on the NEU and GC10-DET steel surface defect benchmarks demonstrate that the proposed method achieves 76.3% mAP@0.5 on NEU (+2.2% over RT-DETR-R18 baseline) and 64.4% mAP@0.5 on GC10-DET (+3.3% over baseline), achieving competitive or superior performance over YOLOv11m on both datasets while requiring only 56.0 GFLOPs and 19.8M parameters. Ablation studies confirm that increasing direction count from 1 to 5 raises mAP from 74.4% to 76.3%, expansion ratio λ=4 is optimal, and CFPD global deployment outperforms selective deployment by +0.9% mAP.

摘要： 钢铁表面缺陷的各向异性纹理特征显著，类内差异大，现有实时检测方法在特征金字塔网络融合阶段对此类方向性局部纹理的感知能力普遍不足。针对这一问题，以RT-DETR为基础框架，提出面向工业表面缺陷检测的方向感知异构卷积特征增强方法，包含三项核心设计：（1）提出方向感知稀疏卷积核DASC（Direction-Aware Sparse Convolution），将通道按方向分组并施加固定稀疏空间掩码，在FPN标准通道配置（C=256）下计算量约为等规格标准卷积的11.5%，可并行感知右、左、下、上及中心5个方向的局部纹理；（2）构建交互瓶颈DIR（Direction-aware Interaction and Refinement），采用扩展—激活—压缩的双层DASC结构，实现通道间方向特征的层次化融合，搭建LFEC3-RT（Lightweight Feature Enhancement module with Cross-stage 3 modules for RT-DETR）特征增强模块；（3）提出跨尺度特征金字塔一致性部署策略CFPD（Cross-scale FPN Consistent Deployment），将LFEC3-RT全局覆盖特征金字塔4个融合位置，消除选择性部署引起的跨尺度特征风格不一致现象。在NEU和GC10-DET两个钢铁表面缺陷基准上的实验表明：NEU上mAP@0.5为76.3%（较基线RT-DETR-R18提升2.2个百分点），GC10-DET上为64.4%（提升3.3个百分点），与YOLOv11m等主流方法性能相当或更优，计算量仅56.0 GFLOPs，参数量19.8M，在检测精度与计算效率间实现良好平衡。消融实验表明，方向数由1增至5时mAP从74.4%提升至76.3%，扩展比λ=4为最优，CFPD全局部署较选择性部署提升0.9%。